In the biometric process of fingerprint scanning, a minutia is a specific biological characteristic in a fingerprint image. Basically, there are two main types of minutiae, known as ridge endings and ridge bifurcations; further known minutiae types are e.g. crossovers, islands, lakes, and spurs. For further details, reference may be made, for example, to R. Basal et al. in “Minutiae Extraction from Fingerprint Images—a Review”, IJCSI International Journal of Computer Science Issues, Volume 8, Issue 5, Number 3, September 2011, ISSN (Online): 1694-0814. It is also possible to consider other fingerprint details as minutiae such as points at which scars begin or terminate. The number and locations of minutiae vary from finger to finger in any particular person, as well as from person to person for any particular finger. When a fingerprint image is obtained from a person, minutiae data can be recorded by determining the precise locations of the minutiae in the form of numerical coordinates and the precise direction of the minutiae in the form of numerical degrees of angle. The result is a fingerprint minutiae template with a fingerprint minutiae data structure.
The fingerprint template data comprises the recorded minutiae data having the predetermined minutiae data structure. The fingerprint minutiae template can be entered and stored, e.g. in a computer database. Thereby, any particular fingerprint minutiae template can be compared with that of anyone else in the world whose fingerprint minutiae template has been stored, as well, for fingerprint recognition.
The comparison may be done by means of a correspondingly programmed computer implementing a fingerprint comparator.
For the fingerprint recognition, currently several fingerprint comparators exist that exploit solely minutiae features. For example, there is the NIST Biometric Image Software (NBIS) distribution developed by the National Institute of Standards and Technology (NIST) for the Federal Bureau of Investigation (FBI) and Department of Homeland Security (DHS) in the United States of America. The NBIS utilities cover eight general categories, which inter alia comprise a fingerprint comparison algorithm called BOZORTH3 that is minutiae based fingerprint comparison algorithm that does both one-to-one and one-to-many comparison operations. A further example is the minutiae cylinder code (MCC) algorithm described, for example, by Raffaele Cappelli et al. in “Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 32, number 12, pages 2128-2141, December, 2010.
The advantage of the standard fingerprint minutiae templates based on a standard data format (ANSI or ISO versions, such as ISO/IEC 19794-2:2005, ISO/IEC 19794-2:2011, and ANSI INCITS 378-2004) is the compatibility achievable by vendors as long as conforming to the standards. However, the encoding process from a fingerprint image into a fingerprint minutiae template with a particular minutiae data structure format causes information loss and thus fingerprint recognition algorithms relying solely on minutiae data, such as the above mentioned BOZORTH3, are normally less performing compared to those algorithms exploiting extra information, such as e.g. ridge count, ridge density, ridge shape, etc., in addition to minutiae information. Thus, how to achieve a good performance with solely minutiae-based fingerprint comparators is a technical challenge since for many years. The MCC algorithm as a solely minutiae-based fingerprint comparison algorithm has already achieved an excellent performance according to test reports.
U.S. Pat. No. 5,631,971 A discloses a vector based topological fingerprint matching system. US 2004/0042645 A1 discloses a fingerprint recognition method, and fingerprint control method and system. Both U.S. Pat. No. 5,631,971 A and US 2004/0042645 A1 use ridges and other structural information in addition to minutiae data. Further, U.S. Pat. No. 6,766,040 B1 discloses a system and method for capturing, enrolling and verifying a fingerprint. US 2004/0042645 A1 shows in essence an approach using multiple sensors for accuracy improvement. ITBO 20090149 A1 with the title “Metodo di codifica delle minuzie di una impronta digitale e corrispondente metodo di riconoscimento di impronte digitali” (relating to the MCC algorithm) and also U.S. Pat. No. 6,766,040 B1 concern algorithms that rely solely on the minutiae information to achieve high recognition accuracy.
However, simple methods, such as minutiae pair comparisons, as used in the BOZORTH3 algorithm, suffer from low recognition accuracy. Further, the MCC algorithm, while having excellent recognition performance, lacks privacy protection for the generated templates.